Respiratory support equipment false triggering identification method, medium and terminal

Abstract

The invention is suitable for the technical field of medical equipment, and relates to a false triggering identification method for respiratory support equipment, a medium and a terminal.The false triggering identification method comprises the steps that S10, patient flow is filtered, and noise is removed; s20, whether other asynchronous types are generated or not is judged, if yes, other asynchronous types are reminded, and if not, pressure and patient flow data within preset time are sequentially stored; and S30, judging whether an inspiration moment is entered or not in real time, performing correlation coefficient calculation on the stored pressure and the patient flow at the inspiration moment, if the correlation coefficient is greater than a preset threshold value, considering that the pressure is not reduced due to inspiration generated by the patient, and judging as a false triggering type, otherwise, judging as a normal respiratory cycle. The method is simple in process, convenient to operate, small in occupied resource and wide in application range, and improves the false triggering recognition efficiency of the respiratory support equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical equipment, and in particular relates to a method, medium and terminal for identifying false triggering of respiratory support equipment. Background Art

[0002] During mechanical ventilation, when the patient's breathing needs do not match the ventilator setting parameters, it is easy to cause human-machine asynchrony and other phenomena, resulting in poor mechanical ventilation effects and failure to achieve clinical treatment effects. Common types of human-machine asynchrony include double triggering, no triggering, early switching, false triggering, etc. False triggering refers to the inhalation being triggered by a decrease in airway pressure or a change in flow that is not caused by the patient's inspiratory efforts. Its most obvious feature is that when maintaining the expiratory pressure, there is no pressure drop caused by inspiratory efforts, and compared with the previous normal breathing cycle, the falsely triggered breathing cycle will produce a smaller tidal volume due to the lack of inspiratory effort.

[0003] The patent application with publication number CN115068760A provides a method for intelligent identification and classification of ventilator-patient ventilation asynchronous phenomenon, including: step 1: obtaining waveform data of the ventilator during mechanical ventilation, and calibrating the waveform data, calibrating the start time of inspiration and the start time of exhalation of each cycle; obtaining mechanical ventilation simulation data under different patient parameters based on the mechanical ventilation model of active breathing patients; using the waveform data and the mechanical ventilation simulation data to build a database; step 2: resampling the database according to the set sampling frequency, and then standardizing and batch processing the sampled data in turn; step 3: using the deep learning algorithm of the UNet neural network to establish the patient's autonomous breathing according to the processed sampled data Inhalation recognition model; Step 4: Collect the waveform data to be identified and the control parameters of the ventilator to be identified, and obtain the respiratory support trigger time and the inhalation-exhalation switching time of each cycle of the ventilator to be identified according to the control parameters; Step 5: Input the waveform data to be identified into the patient's spontaneous breathing recognition model, and calculate the spontaneous breathing starting point timestamp of the patient corresponding to the ventilator to be identified; the spontaneous breathing starting point timestamp includes the patient's physiological spontaneous inhalation start time and spontaneous exhalation start time; calculate the time difference between the patient's spontaneous breathing starting point timestamp and the respiratory support trigger time and the inhalation-exhalation switching time; Step 6: According to the preset human-machine asynchronous phenomenon classification standard, compare and distinguish with the time difference and the waveform data to be identified to obtain the intelligent classification result. This patent application identifies false triggers, but this solution is difficult to implement in embedded software and needs to be connected to an independent server or platform. There is also a method for identification based on respiratory cycle parameters in the prior art. This method requires the patient to maintain a basically consistent respiratory frequency, and the application scenario is limited.

[0004] Therefore, how to provide a respiratory support device false trigger identification method with low resource consumption and wide application range to improve the efficiency of respiratory support device false trigger identification is an urgent problem to be solved by personnel in this technical field. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for identifying false triggering of respiratory support equipment, so as to solve the problem of low efficiency of false triggering identification of respiratory support equipment due to limited resources and application scenarios in the process of false triggering identification of respiratory support equipment in the prior art; in addition, the present invention also provides a false triggering identification medium and terminal for respiratory support equipment.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for identifying false triggering of a respiratory support device, comprising the following steps:

[0008] S10. Filter the patient flow and remove noise; S20. Determine whether other asynchronous types are generated. If so, remind other asynchronous types. If not, store the pressure and patient flow data within the preset time in sequence; S30. Determine in real time whether it has entered the inhalation moment, and calculate the correlation coefficient of the stored pressure and patient flow at the inhalation moment. If the correlation coefficient is greater than the preset threshold, it is considered that the pressure has not dropped due to the patient's inhalation, and it is judged as a false trigger type. Otherwise, it is judged as a normal breathing cycle.

[0009] Furthermore, in step S10, FIR low-pass filtering and one-dimensional Kalman filtering are used to remove noise.

[0010] Furthermore, in step S10, the noise removed is the noise above 5 Hz.

[0011] Furthermore, in step S20, the preset time is 200 ms.

[0012] Furthermore, in step S20, the stored pressure and patient flow data are greater than 20.

[0013] Furthermore, in step S30, the preset threshold is 0.7.

[0014] In a second aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0015] In a third aspect, the present invention further provides an electronic terminal, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method as described above.

[0016] Compared with the prior art, the respiratory support device false trigger identification method, medium and terminal provided by the present invention have at least the following beneficial effects:

[0017] In the prior art, when the patient's breathing needs do not match the ventilator setting parameters, it is easy to cause human-machine asynchrony and other phenomena, resulting in poor mechanical ventilation effects, failure to achieve clinical treatment effects, and low human-machine asynchrony recognition efficiency. The present invention has a simple process, convenient operation, low resource consumption, and a wide range of applications. By analyzing relevant waveform features, false triggers are identified, thereby improving the invalid trigger recognition efficiency of respiratory support equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the scheme of the present invention, a brief introduction is given below to the figures required for use in the description of the embodiments. Obviously, the figures described below are some embodiments of the present invention. For ordinary technicians in this field, other figures can be obtained based on these figures without paying any creative work.

[0019] Figure 1 A flowchart of a method for identifying false triggering of a respiratory support device provided by an embodiment of the present invention;

[0020] Figure 2 A false triggering patient flow and pressure waveform diagram of a respiratory support device false triggering identification method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] The present invention provides a respiratory support device false trigger identification method, which is applied to the identification process of human-machine asynchronous phenomenon in the respiratory support device. The respiratory support device false trigger identification method comprises the following steps:

[0024] S10. Filter the patient flow and remove noise; S20. Determine whether other asynchronous types are generated. If so, remind other asynchronous types. If not, store the pressure and patient flow data within the preset time in sequence; S30. Determine in real time whether it has entered the inhalation moment, and calculate the correlation coefficient of the stored pressure and patient flow at the inhalation moment. If the correlation coefficient is greater than the preset threshold, it is considered that the pressure has not dropped due to the patient's inhalation, and it is judged as a false trigger type. Otherwise, it is judged as a normal breathing cycle.

[0025] The present invention has a simple process, convenient operation, small resource occupation, wide application range, and improves the false triggering recognition efficiency of respiratory support equipment.

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0027] The present invention provides a method for identifying false triggering of a respiratory support device, which is applied to the identification process of human-machine asynchronous phenomenon in the respiratory support device. When the user does not make an inhalation effort, the proximal pressure monitored by the ventilator is basically maintained at the expiratory pressure. If the user inhales normally, the user flow increases. At this time, the turbine control does not have time to respond, and the pressure will drop. Therefore, the correlation coefficient between the patient flow and the proximal pressure of the ventilator in a period of time before the triggering moment (continuous triggering time) is less than 0, that is, the pressure drops and the flow increases; if a false trigger occurs, such as Figure 1 As shown in the figure, the most obvious feature is that the flow and pressure rise at the same time, and the correlation coefficient between the patient flow and the proximal pressure of the ventilator in the period before the inspiratory moment (continuous triggering time) is greater than 0, or even greater than a larger threshold. Figure 1 and Figure 2 In this embodiment, the respiratory support device false trigger identification method includes the following steps:

[0028] S10. Filter the patient flow and pressure, mainly to remove noise.

[0029] Specifically, in this embodiment, noise above 5 Hz is removed.

[0030] Furthermore, in this embodiment, noise removal can be performed by using FIR low-pass filtering, one-dimensional Kalman filtering, etc.

[0031] S20, determining whether other asynchronous types are generated, such as early switching, etc., if so, reminding other asynchronous types, if not, sequentially storing the pressure and patient flow data within a preset time.

[0032] Specifically, in this embodiment, the pressure and patient flow data of the most recent 200 ms (which can be adjusted according to the triggering time) are stored in sequence.

[0033] Furthermore, in this embodiment, the stored pressure and patient flow data are greater than 20.

[0034] S30. Determine in real time whether the inhalation moment has arrived, and calculate the correlation coefficient of the stored pressure and patient flow at the inhalation moment. If the correlation coefficient is greater than the preset threshold, it is considered that the pressure has not dropped due to the inhalation generated by the patient, and it is judged as a false trigger type. Otherwise, it is judged as a normal breathing cycle.

[0035] Specifically, in this embodiment, the preset threshold is 0.7, which can be adjusted according to actual conditions.

[0036] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any one of the methods in the present embodiment is implemented.

[0037] An embodiment of the present invention further provides an electronic terminal, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0038] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0039] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0040] Compared with the prior art, the respiratory support equipment false trigger identification method, medium and terminal described in the above embodiments are easy to cause human-machine asynchrony and other phenomena when the patient's breathing demand does not match the ventilator setting parameters, resulting in poor mechanical ventilation effect, inability to achieve clinical treatment effect, and low human-machine asynchrony identification efficiency. The present invention has a simple process, convenient operation, low resource consumption, and a wide range of applications. By analyzing relevant waveform characteristics, false triggers are identified, thereby improving the invalid trigger identification efficiency of respiratory support equipment.

[0041] Obviously, the embodiments described above are only preferred embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are shown in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A method for identifying false triggering of a respiratory support device, characterized in that: The following steps are involved: S10, filtering the patient flow and removing noise; S20, determining whether other asynchronous types are generated, if so, reminding other asynchronous types, if not, sequentially storing the pressure and patient flow data within a preset time; S30. Determine in real time whether the inhalation moment has arrived, and calculate the correlation coefficient of the stored pressure and patient flow at the inhalation moment. If the correlation coefficient is greater than the preset threshold, it is considered that the pressure has not dropped due to the inhalation generated by the patient, and it is judged as a false trigger type. Otherwise, it is judged as a normal breathing cycle.

2. A method for identifying invalid triggering of a respiratory support device according to claim 1, characterized in that: In the step S10, FIR low-pass filtering and one-dimensional Kalman filtering are used to remove noise.

3. A method for identifying invalid triggering of a respiratory support device according to claim 1, characterized in that: In the step S10, the noise removed is the noise above 5 Hz.

4. A method for identifying invalid triggering of a respiratory support device according to claim 1, characterized in that: In the step S20, the preset time is 200 ms.

5. A method for identifying invalid triggering of a respiratory support device according to claim 1, characterized in that: In step S20, the stored pressure and patient flow data are greater than 20.

6. A method for identifying invalid triggering of a respiratory support device according to claim 1, characterized in that: In step S30, the preset threshold is 0.

7.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

8. An electronic terminal, characterized in that: include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method according to any one of claims 1 to 6.

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